Automated analysis of systems in a large data center, for example, root cause analysis of performance problems in complex applications based on monitored metrics, is a challenging problem. Typically, such analysis, if automated, is carried out using ad hoc techniques and custom approaches, which can pose challenges when the system evolves in size and complexity from the perspective of keeping the logic up-to-date. Accordingly, a need exists for a framework where such analysis is knowledge-based, the framework provides a variety of methods to facilitate the continuous update of knowledge that forms the core of the system, and the analysis and automation modules are based primarily on the principle of interpreting declarative representations of knowledge rather than the execution of standard programming logic to capture IT automation domain knowledge.
In such a system, domain knowledge in the form of a metamodel would be advantageously discovered and updated continuously in a semi-automated manner by tools under the guidance of knowledge engineers. Further, the state of the data center (the topology of infrastructure as well as application elements) can be discovered, monitored, and updated continuously and represented as a model that is an instantiation of the metamodel. Model-based analysis can advantageously leverage the dynamically created data center model, as well as the knowledge embedded in the metamodel, to determine the root cause of problems and facilitate corrective actions to be performed automatically (also leveraging the metamodel) to enable autonomic management of the data center.